Hospital in North Carolina using M2SYS biometric technology

Hugh Chatham Memorial Hospital, a non-profit health facility in North Carolina, announced that it has adopted the RightPatient multi-biometric patient identification system with iris recognition from M2SYS as its preferred biometric modality.

RightPatient was launched in the outpatient admissions and radiology departments with future plans for expansion to the emergency room and physician practices.

“We have been pleased with our decision to switch from our old system to the RightPatient biometric patient identification system,” said Lee Powe, Director of Information Systems at Hugh Chatham Memorial Hospital. “The system offers a variety of very unique components and the installation process was quick and easy, requiring very little internal resources from our end. Plus, the technology has been well received by patients and staff, is extremely easy to use and truly guards patients against medical identity theft and the creation of duplicate medical records. We really liked the fact that a photo is linked to each patient’s record so admissions staff, nurses and other healthcare professionals throughout the hospital can visually verify a patient’s identity at every touch point.”

Initially, Hugh Chatham had invested in a biometric patient identification system that only supported palm vein biometrics, locking the hospital to a single biometric modality and biometric device. That biometric system relied on “one-to-few” segmented biometric searches, which could not entirely prevent duplicate medical records or identity fraud. Furthermore, the old system’s palm vein scanner required physical contact, causing hygiene concerns within the hospital.

Conversely, the RightPatient biometric patient identification system requires no physical contact and performs a fast “one-to-many” biometric search during patient enrollment and identification, comparing the captured biometric template against all stored templates, thereby completely preventing duplicate medical records and eliminate fraud. The system also provides the option of saving biometric images to establish a concrete audit trail of patient authentication activity.

“For those who are still unaware of biometric patient identification systems, this is the time to educate yourself on how this technology can be leveraged to raise patient safety levels, prevent duplicate medical records, eliminate medical identity theft and lower hospital liability,” commented Mizan Rahman, Founder and CEO of M2SYS Technology. “We are at a critical juncture right now in healthcare where hospitals and medical facilities need to completely understand the technology available for patient identification and how it actually works. I am very pleased that we are able to help Hugh Chatham Memorial Hospital enhance their patient safety initiatives.”

RightPatient is the industry’s only multi-modal biometric patient identification system that supports fingerprint, finger vein, palm vein, iris and face recognition. M2SYS Technology, the company that developed RightPatient, brings a decade of diverse biometric technology experience to the healthcare industry. With over 100 million enrolled users in more than 90 countries, M2SYS has applied its comprehensive knowledge of biometrics to RightPatient, resulting in a feature-rich solution that overcomes patient identification challenges in an innovative and practical manner.

Many thanks for covering this important deployment. As more healthcare facilities understand the value of using a hygienic biometric modality like iris recognition for patient identification, we will see them adopt patient identification systems that prevent duplicate medical records, eliminate medical ID theft at the point of enrollment and raise patient safety levels.

1:N #biometric matching can only “completely prevent duplicate medical records” if (a) all templates are available for comparison at every enrollment station in the network, and (b) the matching algorithm has a zero False Accept Rate (FAR).

So, what is the FAR of the RightPatient system?
And what is the corresponding False Reject Rate (FRR)?

There is a tradeoff in all biometrics where FRR goes up as FAR goes down. [It’s basically a sensitivity-specificity tradeoff, which is a term well known amongst healthcare professionals who know it well in cancer screening tests for example]. This FAR-FRR tradeoff is especially acute in 1-to-Many matching because even a tiny FAR causes excessive false positives due to the exponentially large number of pairs in the database. Biometric vendors are rarely candid with their error rates. When making claims of “complete prevention of duplicates” the vendors owe it to us all to reveal their real accuracy specs so that real world risk management is possible.
The first rule of security is this: There is no such thing as perfect security.

1:N #biometric matching can only “completely prevent duplicate medical records” if (a) all templates are available for comparison at every enrollment station in the network, and (b) the matching algorithm has a zero False Accept Rate (FAR).

So, what is the FAR of the RightPatient system?
And what is the corresponding False Reject Rate (FRR)?

There is a tradeoff in all biometrics where FRR goes up as FAR goes down. [It’s basically a sensitivity-specificity tradeoff, which is a term well known amongst healthcare professionals who know it well in cancer screening tests for example]. This FAR-FRR tradeoff is especially acute in 1-to-Many matching because even a tiny FAR causes excessive false positives due to the exponentially large number of pairs in the database. Biometric vendors are rarely candid with their error rates. When making claims of “complete prevention of duplicates” the vendors owe it to us all to reveal their real accuracy specs so that real world risk management is possible.
The first rule of security is this: There is no such thing as perfect security.